discretize converts a numeric vector into a factor with bins having approximately the same number of data points (based on a training set).

discretize(x, ...)

# S3 method for default
discretize(x, ...)

# S3 method for numeric
discretize(
  x,
  cuts = 4,
  labels = NULL,
  prefix = "bin",
  keep_na = TRUE,
  infs = TRUE,
  min_unique = 10,
  ...
)

# S3 method for discretize
predict(object, new_data, ...)

Arguments

x

A numeric vector

...

Options to pass to stats::quantile() that should not include x or probs.

cuts

An integer defining how many cuts to make of the data.

labels

A character vector defining the factor levels that will be in the new factor (from smallest to largest). This should have length cuts+1 and should not include a level for missing (see keep_na below).

prefix

A single parameter value to be used as a prefix for the factor levels (e.g. bin1, bin2, ...). If the string is not a valid R name, it is coerced to one.

keep_na

A logical for whether a factor level should be created to identify missing values in x.

infs

A logical indicating whether the smallest and largest cut point should be infinite.

min_unique

An integer defining a sample size line of dignity for the binning. If (the number of unique values)/(cuts+1) is less than min_unique, no discretization takes place.

object

An object of class discretize.

new_data

A new numeric object to be binned.

Value

discretize returns an object of class discretize and predict.discretize returns a factor vector.

Details

discretize estimates the cut points from x using percentiles. For example, if cuts = 3, the function estimates the quartiles of x and uses these as the cut points. If cuts = 2, the bins are defined as being above or below the median of x.

The predict method can then be used to turn numeric vectors into factor vectors.

If keep_na = TRUE, a suffix of "_missing" is used as a factor level (see the examples below).

If infs = FALSE and a new value is greater than the largest value of x, a missing value will result.

Examples

library(modeldata) data(biomass) biomass_tr <- biomass[biomass$dataset == "Training",] biomass_te <- biomass[biomass$dataset == "Testing",] median(biomass_tr$carbon)
#> [1] 47.1
discretize(biomass_tr$carbon, cuts = 2)
#> Bins: 3 (includes missing category) #> Breaks: -Inf, 47.1, Inf
discretize(biomass_tr$carbon, cuts = 2, infs = FALSE)
#> Bins: 3 (includes missing category) #> Breaks: 14.61, 47.1, 97.18
discretize(biomass_tr$carbon, cuts = 2, infs = FALSE, keep_na = FALSE)
#> Bins: 2 #> Breaks: 14.61, 47.1, 97.18
discretize(biomass_tr$carbon, cuts = 2, prefix = "maybe a bad idea to bin")
#> Warning: The prefix 'maybe a bad idea to bin' is not a valid R name. It has been changed to 'maybe.a.bad.idea.to.bin'.
#> Bins: 3 (includes missing category) #> Breaks: -Inf, 47.1, Inf
carbon_binned <- discretize(biomass_tr$carbon) table(predict(carbon_binned, biomass_tr$carbon))
#> #> bin_missing bin1 bin2 bin3 bin4 #> 0 114 115 113 114
carbon_no_infs <- discretize(biomass_tr$carbon, infs = FALSE) predict(carbon_no_infs, c(50, 100))
#> [1] bin4 <NA> #> Levels: bin_missing bin1 bin2 bin3 bin4
rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur, data = biomass_tr) rec <- rec %>% step_discretize(carbon, hydrogen) rec <- prep(rec, biomass_tr) binned_te <- bake(rec, biomass_te) table(binned_te$carbon)
#> #> bin_missing bin1 bin2 bin3 bin4 #> 0 22 17 25 16